Enhancing Precision Agriculture Efficiency Through Edge Computing-Enabled Wireless Sensor Networks: A Data Aggregation Perspective †

Abstract

Precision agriculture (PA), leveraging wireless sensor networks (WSNs) for efficient data collection, is set to revolutionize intelligent farming. However, challenges such as energy efficiency, data collection time, data quality, redundant data transmission, latency, and limited WSN lifespan persist. We propose a novel edge computing-driven WSN framework (ECDWF) for PA, designed to enhance network longevity by optimizing data transmission to the base station (BS) and enhancing energy dissipation by abolishing data redundancy through aggregation. This framework involves a two-step data aggregation process: within clusters, where the cluster head (CH) aggregates data, and at a central network point, where an edge computing-enabled gateway node (GN) performs further aggregation. Our MATLAB simulation evaluates the proposed ECDWF against the Low-energy adaptive clustering hierarchy (LEACH) protocol and two classic sensing strategies, Effective Node Sensing (ENS) and Periodically Sensing with All Nodes (PSAN). Results reveal significant energy efficiency, quality of data (QoD) transmission, and network lifespan improvements. Due to reduced long-range transmissions, nodes in our scheme dissipate energy over 2500 rounds, compared to 1000 rounds in LEACH. Our method sends data packets to the CH and base station (BS) for 2500 rounds at 3.6 × 1010 bits, while LEACH stops at 1000 rounds at 2 × 1010 bits data transmission rate. Our approach improves network stability and lifetime, with the first node dying at 2070 rounds, versus 999 rounds in LEACH, and the last node remaining functional until 2476 rounds compared to 1000 rounds in LEACH. Our proposed system, ECDWF, outperforms PSAN and ENS in latency, data collection time (DCT), and energy usage. At 50 Mbps, the communication latency of ECDWF is just 8 s, compared to 24 s for ENS and 45 s for PSAN. ECDWF maintains a QoD of 100% across various valid sensor and node counts, surpassing ENS and PSAN. Our contribution integrates edge computing with WSN for PA, enhancing energy utilization and data aggregation. This approach effectively tackles data redundancy, transmission efficiency, and network longevity, providing a robust solution for precision agriculture.